A recently published study in Nature Genetics set out to identify the protein responsible for the genetic variations associated with liver inflammation and scarring. We spoke to Dr Mohammed Eslam, from the Westmead Institute, to learn more about the study and how this finding could help pave the future of diagnosis and treatment for patients.
AM: You recently identified the protein responsible for liver fibrosis. Can you tell us more about this discovery?
ME: In 2015, we identified that common genetic variations associated with liver inflammation and fibrosis (scarring) were located on chromosome 19 between the IFNL3 and IFNL4 genes. However, the causative protein of this genetic area association with inflammation and fibrosis was obscure. This information was critical for any further trials to translate this finding into a potential therapeutic option. In our latest work, we discovered that IFNλ-3 is the causative protein of hepatic inflammation and fibrosis.
Full details of the study can be found here.
AM: What implications does this study have for the future treatment of liver fibrosis?
ME: Now that we’ve identified IFNL3 as the cause of liver scarring, we can work towards developing novel treatments specifically targeting this gene. This could be medicine targeting IFNL3 that is tailored to an individual’s genetic makeup, but could also include modifying usual treatment depending on whether a patient has IFNL3 risk genes. Furthermore, this could be possibly even helpful in scarring in other organs such as the heart, lung and kidneys. Overall, these outcomes fulfil several promises in the modern era of precision medicine.
AM: What are some of the current challenges of detecting liver fibrosis in patients?
ME: A liver biopsy, which is a procedure in which a small needle is inserted into the liver to collect a tissue sample, is still the golden standard of assessment of liver biopsies. However, due to the limitations of this method, an active area of research is to find a non-invasive method which can predict liver fibrosis with a high degree of accuracy, with some options is currently available. Also, another challenge is the ability to predict the patient’s fibrosis progression rates (i.e. slow or fast) rather than just the fibrosis level at particular time point.
AM: Can you tell us about the diagnostic tool you have developed, and how this will help clinicians?
ME: To translate these findings and using machine learning techniques, we have designed a diagnostic tool that incorporates IFNL3 genotyping with other simple clinical variables, which is freely available (www.fibrogene.com) for all doctors to use, to aid in predicting liver fibrosis risk.
AM: What future work do you have planned?
ME: Our team is working to extend this work to further understand the fundamental mechanisms of how IFNL3 contributes to liver disease progression and hopefully we could translate these findings into new therapeutic treatments.
Mohammed Eslam was speaking to Anna MacDonald, Editor for Technology Networks.